

### POLITICAL BEHAVIOR ARTICLE (MINORITY LINKED FATE AND POLITICAL PARTICIPATION)
# AUTHORS: NATHAN KAR MING CHAN AND FRANCISCO JASSO #

#LOAD IN PACKAGES
library(haven)
library(MASS)
library(psy)
library(Zelig)
library(ZeligChoice)
library(readxl)
library(stargazer)
library(readr)
library(ggplot2)

#### LOAD IN DATA
#cmps <- read.csv("C:/Users/natha/Dropbox/Political Behavior_CMPS DATA.csv")


#recode variables
table(cmps$c349_2)
cmps$blackcoethneigh <- NA
cmps$blackcoethneigh <- as.numeric(cmps$blackcoethneigh)
cmps$blackcoethneigh <- ((cmps$c349_2)/100)
table(cmps$blackcoethneigh)

cmps$asiancoethneigh <- NA
cmps$asiancoethneigh <- as.numeric(cmps$asiancoethneigh)
cmps$asiancoethneigh <- ((cmps$c349_4)/100)
table(cmps$c349_4)
table(cmps$asiancoethneigh)

cmps$latcoethneigh <- NA
cmps$latcoethneigh <- as.numeric(cmps$latcoethneigh)
cmps$latcoethneigh <- ((cmps$c349_3)/100)
table(cmps$c349_3)
table(cmps$latcoethneigh)

cmps$civicorg <- NA
cmps$civicorg <- as.numeric(cmps$civicorg)
cmps$civicorg[cmps$c53==1] <- 0.5
cmps$civicorg[cmps$c53==2] <- 1
cmps$civicorg[cmps$c53==3] <- 0
table(cmps$c53)
table(cmps$civicorg)

cmps$civicpart <- NA
cmps$civicpart <- as.numeric(cmps$civicpart)
cmps$civicpart[cmps$c53==2&cmps$c54==1] <- 1
cmps$civicpart[cmps$c53==1&cmps$c54==1] <- 0.75
cmps$civicpart[cmps$c53==2&cmps$c54==2] <- 0.5
cmps$civicpart[cmps$c53==1&cmps$c54==2] <- 0.25
cmps$civicpart[cmps$c53==3] <- 0
table(cmps$civicpart)

cmps$income <- NA
cmps$income <- as.numeric(cmps$income)
cmps$income[cmps$c383==1] <- 0
cmps$income[cmps$c383==2] <- 0.09
cmps$income[cmps$c383==3] <- 0.18
cmps$income[cmps$c383==4] <- 0.27
cmps$income[cmps$c383==5] <- 0.36
cmps$income[cmps$c383==6] <- 0.45
cmps$income[cmps$c383==7] <- 0.54
cmps$income[cmps$c383==8] <- 0.63
cmps$income[cmps$c383==9] <- 0.72
cmps$income[cmps$c383==10] <- 0.81
cmps$income[cmps$c383==11] <-0.9
cmps$income[cmps$c383==12] <- 1
table(cmps$income)

cmps$education <- NA
cmps$education <- as.numeric(cmps$education)
cmps$education[cmps$c381==1] <- 0
cmps$education[cmps$c381==2] <- 0.2
cmps$education[cmps$c381==3] <- 0.4
cmps$education[cmps$c381==4] <- 0.6
cmps$education[cmps$c381==5] <- 0.8
cmps$education[cmps$c381==6] <- 1
table(cmps$education)

cmps$polinterest <- NA
cmps$polinterest <- as.numeric(cmps$polinterest)
cmps$polinterest[cmps$interest==3] <- 1
cmps$polinterest[cmps$interest==2] <- 0.667
cmps$polinterest[cmps$interest==1] <- 0.333
cmps$polinterest[cmps$interest==0] <- 0
table(cmps$polinterest)

cmps$age.new <- NA
cmps$age.new <- as.numeric(cmps$age)/100
table(cmps$age.new)

cmps$intefficacy <- NA
cmps$intefficacy <- as.numeric(cmps$intefficacy)
cmps$intefficacy[cmps$efficacyInt==5] <- 1
cmps$intefficacy[cmps$efficacyInt==4] <- 0.75
cmps$intefficacy[cmps$efficacyInt==3] <- 0.5
cmps$intefficacy[cmps$efficacyInt==2] <- 0.25
cmps$intefficacy[cmps$efficacyInt==1] <- 0
table(cmps$intefficacy)

cmps$extefficacy <- NA
cmps$extefficacy <- as.numeric(cmps$extefficacy)
cmps$extefficacy[cmps$efficacyExt==1] <- 0
cmps$extefficacy[cmps$efficacyExt==2] <- 0.25
cmps$extefficacy[cmps$efficacyExt==3] <- 0.5
cmps$extefficacy[cmps$efficacyExt==4] <- 0.75
cmps$extefficacy[cmps$efficacyExt==5] <- 1
table(cmps$extefficacy)

cmps$forborn <- NA
cmps$forborn <- as.numeric(cmps$forborn)
cmps$forborn[cmps$s7==1] <- 0
cmps$forborn[cmps$s7==2] <- 1
cmps$forborn[cmps$s7==3] <- 1
table(cmps$forborn)

cmps$catholic <- NA
cmps$catholic <- as.numeric(cmps$catholic)
cmps$catholic[cmps$c129==1] <- 1
cmps$catholic[cmps$c129==2] <- 0
cmps$catholic[cmps$c129==3] <- 0
cmps$catholic[cmps$c129==4] <- 0
cmps$catholic[cmps$c129==5] <- 0
cmps$catholic[cmps$c129==6] <- 0
cmps$catholic[cmps$c129==7] <- 0
cmps$catholic[cmps$c129==8] <- 0
cmps$catholic[cmps$c129==9] <- 0
table(cmps$c129)
table(cmps$catholic)

cmps$protestant <- NA
cmps$protestant <- as.numeric(cmps$protestant)
cmps$protestant[cmps$c129==1] <- 0
cmps$protestant[cmps$c129==2] <- 1
cmps$protestant[cmps$c129==3] <- 0
cmps$protestant[cmps$c129==4] <- 0
cmps$protestant[cmps$c129==5] <- 0
cmps$protestant[cmps$c129==6] <- 0
cmps$protestant[cmps$c129==7] <- 0
cmps$protestant[cmps$c129==8] <- 0
cmps$protestant[cmps$c129==9] <- 0
table(cmps$c129)
table(cmps$protestant)

cmps$christian <- NA
cmps$christian <- as.numeric(cmps$christian)
cmps$christian[cmps$c129==1] <- 1
cmps$christian[cmps$c129==2] <- 1
cmps$christian[cmps$c129==3] <- 1
cmps$christian[cmps$c129==4] <- 0
cmps$christian[cmps$c129==5] <- 0
cmps$christian[cmps$c129==6] <- 0
cmps$christian[cmps$c129==7] <- 0
cmps$christian[cmps$c129==8] <- 0
cmps$christian[cmps$c129==9] <- 0
table(cmps$c129)
table(cmps$christian)

cmps$republican <- NA
cmps$republican <- as.numeric(cmps$republican)
cmps$republican[cmps$c25==1] <- 1
cmps$republican[cmps$c25==2] <- 0
cmps$republican[cmps$c25==3] <- 0
cmps$republican[cmps$c25==4] <- 0
table(cmps$republican)

cmps$conservative <- NA
cmps$conservative <- as.numeric(cmps$conservative)
cmps$conservative[cmps$c31==1] <- 0
cmps$conservative[cmps$c31==2] <- 0
cmps$conservative[cmps$c31==3] <- 0
cmps$conservative[cmps$c31==4] <- 1
cmps$conservative[cmps$c31==5] <- 1
cmps$conservative[cmps$c31==6] <- 0

cmps$churchattendance <- NA
cmps$churchattendance <- as.numeric(cmps$churchattendance)
cmps$churchattendance[cmps$c131==1] <- 1
cmps$churchattendance[cmps$c131==2] <-0.8
cmps$churchattendance[cmps$c131==3] <-0.6
cmps$churchattendance[cmps$c131==4] <-0.4
cmps$churchattendance[cmps$c131==5] <- 0.2
cmps$churchattendance[cmps$c131==6] <- 0
table(cmps$churchattendance)

table(cmps$bla191)
cmps$minlinkfate <- NA
cmps$minlinkfate <- as.numeric(cmps$minlinkfate)
cmps$minlinkfate[cmps$bla191==1] <- 1
cmps$minlinkfate[cmps$bla191==2] <- 0.667
cmps$minlinkfate[cmps$bla191==3] <- 0.333
cmps$minlinkfate[cmps$bla191==4] <- 0
table(cmps$bla191)
table(cmps$minlinkfate)

table(cmps$linkfate)
cmps$reglinkfate <- NA
cmps$reglinkfate <- as.numeric(cmps$reglinkfate)
cmps$reglinkfate[cmps$linkfate==0] <- 0
cmps$reglinkfate[cmps$linkfate==1] <- 0.333
cmps$reglinkfate[cmps$linkfate==2] <- 0.667
cmps$reglinkfate[cmps$linkfate==3] <- 1
table(cmps$reglinkfate)

cmps$lf <- NA
cmps$lf <- as.numeric(cmps$lf)
cmps$lf[cmps$linkfate==0] <- 0
cmps$lf[cmps$linkfate==1] <- 0.333
cmps$lf[cmps$linkfate==2] <- 0.667
cmps$lf[cmps$linkfate==3] <- 1
table(cmps$lf)

cmps$democrat <- NA
cmps$democrat <- as.numeric(cmps$democrat)
cmps$democrat[cmps$c25==1] <- 0
cmps$democrat[cmps$c25==2] <- 1
cmps$democrat[cmps$c25==3] <- 0
cmps$democrat[cmps$c25==4] <- 0
table(cmps$democrat)

cmps$ppact <- NA
cmps$ppact <- as.numeric(cmps$ppact)
cmps$ppact <- ((cmps$vote)+(cmps$donate)+(cmps$campwork)+
                 (cmps$comm_mtg)+(cmps$comm_work)+(cmps$contactpers)+
                 (cmps$protest)+(cmps$petition)+(cmps$boycott))/9
table(cmps$ppact)

cmps$pptrad <- NA
cmps$pptrad <- as.numeric(cmps$pptrad)
cmps$pptrad <- ((cmps$donate)+(cmps$campwork)+
                  (cmps$comm_mtg)+(cmps$comm_work)+(cmps$contactpers))/5
table(cmps$pptrad)

cmps$ppnew <- NA
cmps$ppnew <- as.numeric(cmps$ppnew)
cmps$ppnew <- ((cmps$protest)+(cmps$petition)+(cmps$boycott))/3
table(cmps$ppnew)


## SUBSET DATA BY RACIAL/ETHNIC GROUPS ## 
asian.american <- subset(cmps, subset=(ethnic_quota==4))
asian.american.citizen <- subset(cmps, subset=(ethnic_quota==4&citizen2==1))

afam <- subset(cmps, subset=(ethnic_quota==3))
afam.citizen <- subset(cmps, subset=(ethnic_quota==3&citizen2==1))

latino <- subset(cmps, subset=(ethnic_quota==2))
latino.citizen <- subset(cmps, subset=(ethnic_quota==2&citizen2==1))

white <- subset(cmps, subset=(ethnic_quota==1))
white.citizen <- subset(cmps, subset=(ethnic_quota==1&citizen2==1))



#### RUN DESCRIPTIVES AND DIFFERENCE OF MEANS TESTS BETWEEN DIFFERENT TYPES OF LINKED FATE BY RACE/ETHNICITY
cor.test(cmps$reglinkfate, cmps$minlinkfate)

t.test(afam$minlinkfate,afam$reglinkfate)
t.test(latino$minlinkfate,latino$reglinkfate)
t.test(asian.american$reglinkfate,asian.american$minlinkfate)

t.test(latino$minlinkfate,afam$minlinkfate)
t.test(afam$minlinkfate,asian.american$minlinkfate)

t.test(afam$reglinkfate,asian.american$reglinkfate)
t.test(latino$reglinkfate,afam$reglinkfate)


t.test(latino$reglinkfate,asian.american$reglinkfate)
t.test(asian.american$minlinkfate,latino$minlinkfate)

### FULL REGRESSION OUTPUT FOR VOTER TURNOUT WITH PREDICTIONS
#BLACK VOTE
black.vote.full <- glm(vote~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                         ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_regorvote+
                         blackcoethneigh+civicpart
                       ,data=afam.citizen, family='binomial')
summary(black.vote.full)

zelig.black.vote<- zelig(vote~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                           ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_regorvote+
                           blackcoethneigh+civicpart
                         ,
                         data=afam.citizen, model="logit")
summary(zelig.black.vote)


x.high.mlf <- setx(zelig.black.vote, minlinkfate=1)
x.low.mlf <- setx(zelig.black.vote, minlinkfate=0)
 
set.seed(1000)
sim.black.votea <- sim(zelig.black.vote, x1=x.high.mlf, x=x.low.mlf)
summary(sim.black.votea)


x.high.rlf <- setx(zelig.black.vote, reglinkfate=1)
x.low.rlf <- setx(zelig.black.vote, reglinkfate=0)

set.seed(1000)
sim.black.voteb <- sim(zelig.black.vote, x1=x.high.rlf, x=x.low.rlf)
summary(sim.black.voteb)


#LATINX VOTE

latinx.vote.full <- glm(vote~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                          ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_regorvote+
                          latcoethneigh+civicpart
                        ,data=latino.citizen, family='binomial')
summary(latinx.vote.full)

zelig.latinx.vote<- zelig(vote~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                            ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_regorvote+
                            latcoethneigh+civicpart
                          ,
                          data=latino.citizen, model="logit")
summary(zelig.latinx.vote)

x.high.mlf <- setx(zelig.latinx.vote, minlinkfate=1)
x.low.mlf <- setx(zelig.latinx.vote, minlinkfate=0)

set.seed(1000)
sim.latinx.votea <- sim(zelig.latinx.vote, x1=x.high.mlf, x=x.low.mlf)
summary(sim.latinx.votea)

x.high.rlf <- setx(zelig.latinx.vote, reglinkfate=1)
x.low.rlf <- setx(zelig.latinx.vote, reglinkfate=0)

set.seed(1000)
sim.latinx.voteb <- sim(zelig.latinx.vote, x1=x.high.rlf, x=x.low.rlf)
summary(sim.latinx.voteb)

#ASIAN AMERICAN VOTE

asian.vote.full <- glm(vote~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                         ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_regorvote+
                         asiancoethneigh+civicpart
                       ,data=asian.american.citizen, family='binomial')
summary(asian.vote.full)

zelig.asian.vote<- zelig(vote~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                           ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_regorvote
                         +asiancoethneigh+civicpart
                         ,
                         data=asian.american.citizen, model="logit")
summary(zelig.asian.vote)

x.high.mlf <- setx(zelig.asian.vote, minlinkfate=1)
x.low.mlf <- setx(zelig.asian.vote, minlinkfate=0)

set.seed(1000)
sim.asian.votea <- sim(zelig.asian.vote, x1=x.high.mlf, x=x.low.mlf)
summary(sim.asian.votea)

x.high.rlf <- setx(zelig.asian.vote, reglinkfate=1)
x.low.rlf <- setx(zelig.asian.vote, reglinkfate=0)

set.seed(1000)
sim.asian.voteb <- sim(zelig.asian.vote, x1=x.high.rlf, x=x.low.rlf)
summary(sim.asian.voteb)


### FULL REGRESSION OUTPUT FOR CONVENTIONAL PARTICIPATION WITH PREDICTIONS FOR GRAPHS

#AFRICAN AMERICAN (TRAD PARTICIPATION)

black.pptrad.p <- glm(pptrad~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                        ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_pty+
                        blackcoethneigh+civicpart, data=afam, family="poisson")
summary(black.pptrad.p)

zelig.black.pptrad<- zelig(pptrad~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                             ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_pty+
                             blackcoethneigh+civicpart
                           ,
                           data=afam, model="poisson")
summary(zelig.black.pptrad)

table(afam$minlinkfate)
x.high.mlf <- setx(zelig.black.pptrad, minlinkfate=1)
x.low.mlf <- setx(zelig.black.pptrad, minlinkfate=0)

set.seed(1000)
sim.black.pptrada <- sim(zelig.black.pptrad, x1=x.high.mlf, x=x.low.mlf)
summary(sim.black.pptrada)

x.high.rlf <- setx(zelig.black.pptrad, reglinkfate=1)
x.low.rlf <- setx(zelig.black.pptrad, reglinkfate=0)

set.seed(1000)
sim.black.pptradb <- sim(zelig.black.pptrad, x1=x.high.rlf, x=x.low.rlf)
summary(sim.black.pptradb)

#LATINX TRAD PARTICIPATION

latinx.pptrad.p <- glm(pptrad~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                         ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_pty+
                         latcoethneigh+civicpart, data=latino, family="poisson")

summary(latinx.pptrad.p)

zelig.latinx.pptrad<- zelig(pptrad~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                              ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_pty
                            +latcoethneigh+civicpart
                            ,
                            data=latino, model="poisson")
summary(zelig.latinx.pptrad)

x.high.mlf <- setx(zelig.latinx.pptrad, minlinkfate=1)
x.low.mlf <- setx(zelig.latinx.pptrad, minlinkfate=0)

set.seed(1000)
sim.latinx.pptrada <- sim(zelig.latinx.pptrad, x1=x.high.mlf, x=x.low.mlf)
summary(sim.latinx.pptrada)

x.high.rlf <- setx(zelig.latinx.pptrad, reglinkfate=1)
x.low.rlf <- setx(zelig.latinx.pptrad, reglinkfate=0)

set.seed(1000)
sim.latinx.pptradb <- sim(zelig.latinx.pptrad, x1=x.high.rlf, x=x.low.rlf)
summary(sim.latinx.pptradb)

#ASIAN AMERICAN TRAD PARTICIPATION

asian.pptrad.p <- glm(pptrad~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                        ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_pty+
                        asiancoethneigh+civicpart, data=asian.american, family="poisson")

summary(asian.pptrad.p)

zelig.asian.pptrad<- zelig(pptrad~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                             ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_pty
                           +asiancoethneigh+civicpart
                           ,
                           data=asian.american, model="poisson")
summary(zelig.asian.pptrad)


x.high.mlf <- setx(zelig.asian.pptrad, minlinkfate=1)
x.low.mlf <- setx(zelig.asian.pptrad, minlinkfate=0)

set.seed(1000)
sim.asian.pptrada <- sim(zelig.asian.pptrad, x1=x.high.mlf, x=x.low.mlf)
summary(sim.asian.pptrada)

x.high.rlf <- setx(zelig.asian.pptrad, reglinkfate=1)
x.low.rlf <- setx(zelig.asian.pptrad, reglinkfate=0)

set.seed(1000)
sim.asian.pptradb <- sim(zelig.asian.pptrad, x1=x.high.rlf, x=x.low.rlf)
summary(sim.asian.pptradb)


##FULL REGRESSION OUTPUT FOR UNCONVENTIONAL PARTICIPATION W/ ZELIG PREDICTIONS

#Black new participation

black.ppunconv.p <- glm(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                          ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_regorvote+
                          blackcoethneigh+civicpart, data=afam, family="poisson")
summary(black.ppunconv.p)

zelig.black.ppnew<- zelig(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                            ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_pty+
                            blackcoethneigh+civicpart
                          ,
                          data=afam, model="poisson")
summary(zelig.black.ppnew)

x.high.mlf <- setx(zelig.black.ppnew, minlinkfate=1)
x.low.mlf <- setx(zelig.black.ppnew, minlinkfate=0)

set.seed(1000)
sim.black.ppnewa <- sim(zelig.black.ppnew, x1=x.high.mlf, x=x.low.mlf)
summary(sim.black.ppnewa)

x.high.rlf <- setx(zelig.black.ppnew, reglinkfate=1)
x.low.rlf <- setx(zelig.black.ppnew, reglinkfate=0)

set.seed(1000)
sim.black.ppnewb <- sim(zelig.black.ppnew, x1=x.high.rlf, x=x.low.rlf)
summary(sim.black.ppnewb)

## LATINX PARTICIPATION NEW
latinx.ppunconv.p <- glm(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                           ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_regorvote+
                           latcoethneigh+civicpart, data=latino, family="poisson")

summary(latinx.ppunconv.p)

zelig.latinx.ppnew<- zelig(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                             ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_pty+
                             latcoethneigh+civicpart
                           ,
                           data=latino, model="poisson")
summary(zelig.latinx.ppnew)

x.high.mlf <- setx(zelig.latinx.ppnew, minlinkfate=1)
x.low.mlf <- setx(zelig.latinx.ppnew, minlinkfate=0)

set.seed(1000)
sim.latinx.ppnewa <- sim(zelig.latinx.ppnew, x1=x.high.mlf, x=x.low.mlf)
summary(sim.latinx.ppnewa)

x.high.rlf <- setx(zelig.latinx.ppnew, reglinkfate=1)
x.low.rlf <- setx(zelig.latinx.ppnew, reglinkfate=0)

set.seed(1000)
sim.latinx.ppnewb <- sim(zelig.latinx.ppnew, x1=x.high.rlf, x=x.low.rlf)
summary(sim.latinx.ppnewb)

#Asian Americans Unconventional Participation
asian.ppunconv.p <- glm(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                          ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_pty+
                          asiancoethneigh+civicpart, data=asian.american, family="poisson")

summary(asian.ppunconv.p)

zelig.asian.ppnew<- zelig(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+
                            ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_pty+
                            asiancoethneigh+civicpart
                          ,
                          data=asian.american, model="poisson")
summary(zelig.asian.ppnew)

x.high.mlf <- setx(zelig.asian.ppnew, minlinkfate=1)
x.low.mlf <- setx(zelig.asian.ppnew, minlinkfate=0)

set.seed(1000)
sim.asian.ppnewa <- sim(zelig.asian.ppnew, x1=x.high.mlf, x=x.low.mlf)
summary(sim.asian.ppnewa)

x.high.rlf <- setx(zelig.asian.ppnew, reglinkfate=1)
x.low.rlf <- setx(zelig.asian.ppnew, reglinkfate=0)

set.seed(1000)
sim.asian.ppnewb <- sim(zelig.asian.ppnew, x1=x.high.rlf, x=x.low.rlf)
summary(sim.asian.ppnewb)


### RUN DIFFERENT MODEL SPECIFICATIONS FOR BLACK PARTICIPATION (NEW)

black.ppunconv.p1 <- glm(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+civicpart+blackcoethneigh, data=afam, family="poisson")
summary(black.ppunconv.p1)

black.ppunconv.p2 <- glm(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+civicpart+blackcoethneigh+
                           ptystn+conservative+republican+polinterest+intefficacy+extefficacy, data=afam, family="poisson")
summary(black.ppunconv.p2)

black.ppunconv.p3 <- glm(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+civicpart+blackcoethneigh+
                           ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_pty, data=afam, family="poisson")
summary(black.ppunconv.p3)

### RUN DIFFERENT MODEL SPECIFICATIONS FOR LATINX PARTICIPATION (NEW)
latinx.ppunconv.p1 <- glm(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+latcoethneigh+civicpart, data=latino, family="poisson")
summary(latinx.ppunconv.p1)

latinx.ppunconv.p2 <- glm(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+latcoethneigh+civicpart+
                            ptystn+conservative+republican+polinterest+intefficacy+extefficacy, data=latino, family="poisson")

summary(latinx.ppunconv.p2)

latinx.ppunconv.p3 <- glm(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+latcoethneigh+civicpart+
                            ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_pty, data=latino, family="poisson")

summary(latinx.ppunconv.p3)


## RUN MODEL SPECIFICATIONS FOR ASIAN AMERICANS UNCONVENTIONAL PARTICIPATION

asian.ppunconv.p1 <- glm(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+asiancoethneigh+civicpart, data=asian.american, family="poisson")
summary(asian.ppunconv.p1)

asian.ppunconv.p2 <- glm(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+asiancoethneigh+civicpart+
                           ptystn+conservative+republican+polinterest+intefficacy+extefficacy, data=asian.american, family="poisson")

summary(asian.ppunconv.p2)

asian.ppunconv.p3 <- glm(ppnew~minlinkfate+reglinkfate+age.new+income+education+female+forborn+asiancoethneigh+civicpart+
                           ptystn+conservative+republican+polinterest+intefficacy+extefficacy+recruit_pty, data=asian.american, family="poisson")

summary(asian.ppunconv.p3)
